{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# State Action Value Function Example\n",
    "\n",
    "In this Jupyter notebook, you can modify the mars rover example to see how the values of Q(s,a) will change depending on the rewards and discount factor changing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do not modify\n",
    "num_states = 6\n",
    "num_actions = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminal_left_reward = 100\n",
    "terminal_right_reward = 40\n",
    "each_step_reward = 0\n",
    "\n",
    "# Discount factor\n",
    "gamma = 0.5\n",
    "\n",
    "# Probability of going in the wrong direction\n",
    "misstep_prob = 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "generate_visualization(terminal_left_reward, terminal_right_reward, each_step_reward, gamma, misstep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
